RESA (Realtime Situational Awareness) is a system designed for real-time scene understanding and reasoning across various sectors, including safety, manufacturing, retail, healthcare, and personal assistance. It continuously monitors and analyzes video, acoustics, and time-series data related to human activities to provide a comprehensive understanding of ongoing situations.

The Realtime Situational Awareness | Video Understanding Project leverages advanced AI models to process large amounts of data, delivering actionable insights through intuitive visualizations. By highlighting significant patterns, trends, and anomalies, RESA empowers users to make quick, accurate decisions in dynamic environments, enhancing response strategies and operational efficiency.

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Adaptive Memory Networks

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Adaptive Memory Networks We present Adaptive Memory Networks (AMN) that processes input-question pairs to dynamically construct a network architecture optimized for lower inference times for Question Answering (QA) tasks. AMN processes the input story to extract entities and stores them in memory banks. Starting from a single bank, as the number of input entities increases, AMN learns to create new banks as the entropy in a single bank becomes too high. Hence, after processing an input-question(s) pair, the resulting network represents a hierarchical structure where entities are stored in different banks, distanced by question relevance. At inference, one or few banks are used, creating a tradeoff between accuracy and performance. AMN is enabled by dynamic networks that allow input dependent network creation and efficiency in dynamic mini-batching as well as our novel bank controller that allows learning discrete decision making with high accuracy. In our results, we demonstrate that AMN learns to create variable depth networks depending on task complexity and reduces inference times for QA tasks.

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